Physical AI needs human data, but human data capture is still way too hard. Not because pressing record is hard. Because the moment you add cameras + sensors, everything gets messy: Every device has its own clock. Streams can silently fail. Recording health has to be checked. Start / stop has to line up. Synchronization has to be solved after. SyncField Desktop turns it into one workflow. Auto-discover cameras + sensors. Connect streams with aliases. Drag, arrange, and monitor panels. Record everything in one click. Review synchronized playback. Get frame-aligned data on disk. No handclaps. No LED flashes. No sync scripts. No file wrangling. Just humans doing real tasks, captured cleanly. If you're working on human data for Physical AI, reach out: opengraphlabs.com/#contact

World models can predict the next frame. They can't predict the next touch. That's the gap visuo-tactile world models will close. Is the robot gripping hard enough? Is the surface rigid or soft? When exactly does contact begin and end? Vision doesn't know. Tactile does. We built @OpenGraph_Labs to capture what cameras miss. Egocentric RGB × 5-finger multi-taxel tactile gloves. Frame-synced. Calibrated. In-the-wild. No lab setups. No scripted pick-and-place. Just humans doing real tasks in real stores. Watch the exact moment contact happens. The pressure map lights up in sync. Every touch. Every frame. 👇

Owning your data infra is the new moat in robotics.

Are you sure your training data is actually synced? Egocentric camera sees a hand grasping an orange, but the wrist cam shows nothing and tactile reads zero contact. Your policy is learning from broken data and doesn't even know it. In Physical AI, multi-modal sync is everything. → Egocentric: 30fps → Wrist: 30fps → Tactile: 100Hz Different devices, different clocks, slightly different rates. The drift starts small. Barely noticeable frame by frame. But over a 4-minute episode, that tiny difference compounds into seconds of misalignment. And you had no way to even check. Until now. We built the Sync Quality Dashboard. One score tells you if your data is clean. Then go deeper. Clock offset, drift rate, jitter, frame drops, per-stream correction. All visible, all measurable. In a 4-min episode, accumulated clock drift reached 7.5 seconds by the end of the recording. After correction: 9.0ms. That's the difference between "roughly aligned" and "actually aligned." Visually confirm vision-to-vision, vision-to-tactile alignment frame by frame. No more "trust me, the data is fine." We don't just collect multi-modal demos. We ship a quality assurance layer so you can verify every episode before it touches your model. All data in @LeRobotHF format. Ready to train. Verified in sync. Stop guessing. Start verifying.

VLMs see everything. Feel nothing. VLMs annotate what looks like contact. Tactile sensors verify what actually is contact. We ran VLM annotation on real manipulation demos. It labeled a grasp as "approach." Skipped release phases entirely. Hallucinated state transitions that never occurred. 6 out of 36 action phases wrong. 17% of your training data, corrupted. Why? Pixels don't know when a fingertip touches a surface. Pixels don't know when grip pressure hits zero. Tactile sensors do. So we built a pipeline that catches every error automatically. Tactile evidence validates every contact transition. Wrong labels corrected, missing phases inserted. Not faster labeling. Truthful labeling. This is one of the core problems we're solving at @OpenGraph_Labs

Robots can't learn if their eyes and hands are out of sync. A 30fps camera and a 1kHz tactile sensor don't speak the same language. Multiple cameras, multiple sensors, all on different clocks at different rates. Jitter from USB and OS scheduling. Drift that compounds every second of recording. We built a multi-modal sync pipeline that aligns all of it to ±2.5ms. Automatically. Every frame matched. Zero sensor samples lost. No hardware triggers needed. Sensor-agnostic. Hardware-agnostic. Just plug in and record. Physical AI needs real hand-eye coordination. Not approximate, precise. We're building this at OpenGraph. opengraphlabs.com/

Now robots can feel the world.

Cloned my real LeRobot SO-ARM101 setup straight into a MuJoCo scene. same robot, same red/blue/yellow cubes, same place zone. Open the browser, hit the keyboard, and you can move the arm instantly. Real robot → MuJoCo twin → web teleop. Anyone can try it. app.opengraphlabs.com #robotics #MuJoCo #LeRobot #teleop

Making a humanoid stand is harder than it looks. It needs a brain, a policy constantly deciding where every joint should be. And fast reflexes, a PD controller correcting errors in real-time. We run both on the server: - Pre-trained RL policy from Unitree's framework → target positions - PD controller → smooth, stable torques User presses arrow keys in browser → WebSocket → server runs inference → robot walks. Why server-side? The policy is a PyTorch model. Running ML in a browser means conversion headaches. On the server, just load and run. Swap policies by replacing one file. No client redeploy. Same code that would run on real hardware. Browser stays lightweight, send commands, receive poses, render frames. Your browser doesn't need to understand robot control. It sends intent, receives results, draws pixels. Server-authoritative = real robotics code in the loop. #Robotics #mujoco #unitree #reinforcementlearning

MuJoCo on server. Three.js in browser. WebSocket in between. Physics and rendering don't need to live together. Streaming robot state, rendering in Three.js, physics running server-side. Benefits: - Swap physics engines without touching renderer - No browser memory ceiling - Precise MuJoCo physics + beautiful Three.js visuals PoC done. Unitree G1 walking demo synced across both views. Working toward a clean streaming protocol. #robotics #mujoco #threejs